• Corpus ID: 54089884

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

@article{Locatello2019ChallengingCA,
  title={Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations},
  author={Francesco Locatello and Stefan Bauer and Mario Lucic and Sylvain Gelly and Bernhard Sch{\"o}lkopf and Olivier Bachem},
  journal={ArXiv},
  year={2019},
  volume={abs/1811.12359}
}
The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. [] Key Method We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data.
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